The present invention relates to medical diagnostic ultrasonic imaging, and in particular to systems that adaptively set gain to improve such imaging.
In conventional ultrasonic imaging, a B-mode image signal is adjusted for gain before the signal is mapped to a range of gray levels or colors for display. The gain can conventionally be varied by the user using depth gain compensation (DGC) or a time gain compensation (TGC) control along with the master gain or B gain control. The DGC and TGC controls are conventionally variable in range only, and the master gain is independent of both range and azimuthal position. However, a lateral gain (LGC) may also be used.
Commercially available one-dimensional gain controls are often used by users to adjust brightness level. In many cases, users adjust the gain mainly to keep the regional mean of the soft tissue gray level within a narrow range of gray values. This preferred range is somewhat consistent from user to user, and in many cases users tend to adjust the gain to set the gray level for soft tissue to the 64th gray level on a linear map that maps 0 to black and 255 to white. However, gain adjustments for soft tissue brightness uniformity do not simultaneously optimize noise suppression and avoidance of saturation effects. Manual gain adjustments take time and require user expertise. Gain is frequently sub-optimal for some or all parts of an image. As a result, information can be lost by cutting off low-level signals or saturating high-level signals.
Various automatic gain setting algorithms have been used. One example is provided for color Doppler energy imaging. A measurement of the thermal noise along a center line is used to set the gain dependence on depth which can provide maximum signal sensitivity regardless of signals responsive to the transmission of acoustic energy. The user cannot adjust this gain, and the gain is not optimized as a function of multiple dimensions.
To more optimally control gain, U.S. Pat. No. 6,398,733 (assigned to the assignee of the present invention) discloses adaptively setting gain for a B-mode image. Spatial variance is used to identify regions of the image corresponding substantially to soft tissue. The system acquires a thermal noise frame with the transmitters turned off, and then uses the thermal noise frame and the identified regions of soft tissue both to locally and adaptively set the gain to cause soft tissue to be displayed at a constant average level throughout the image.
Optimal gain settings are different for imaging contrast agents. The target brightness may be manually adjusted for second harmonic B-mode contrast agent imaging. Gain optimization is important but difficult for imaging contrast agents. Contrast agent imaging may use low transmit powers, making setting the gain for adequate sensitivity difficult. Signals from tissue may be included in the contrast agent image, so the gain may reduce contrast between contrast agents and tissue.
Some contrast agent imaging protocols require brightness level comparison, requiring that the gain not be adjusted from before contrast agents are injected, or at least during the course of agent uptake into or outflow from tissue. Perfusion kinetics (e.g., arrival time, rise-time constant, peak enhancement or others) are quantified in some procedures. The procedures may be repeated during an imaging session by acoustic destruction of contrast agents, so any automatic gain setting is performed prior to the injection of contrast agents to provide consistency.
The various gain setting techniques discussed above for tissue imaging may be sub-optimal for other types of imaging, such as contrast agent imaging, and vice versa. Several different types of imaging are frequently used for imaging contrast agents, such as one image generated to represent contrast agents and another image generated to represent tissue. The contrast agent and tissue images are displayed separately or one overlaid on the other. The same gain curve may be applied for both images. Setting a gain curve based on the tissue image results in a poor gain curve for the contrast agent image. Gain setting algorithms adapted for identifying soft tissue may not be robust or optimal for the gain of the contrast agent image. The character of contrast agent images differs from tissue images. Since contrast agent imaging typically begins before introduction of contrast agent, any initial gain settings may be improper after administration of the contrast agent. Prior to the administration of contrast agent, automatic gain settings based on the contrast agent image may fail due to a lack of signal.